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Head to head

EXAONE 1.0 vs Minerva (540B)

LG
EXAONE 1.0
December 2021
vs
Google
Minerva (540B)
June 2022
1.7×10²⁴Training compute (FLOP)2.7×10²⁴
$3MTraining cost--

EXAONE 1.0 (LG) and Minerva (540B) (Google) are both frontier AI models. EXAONE 1.0 was published in December 2021 and Minerva (540B) in June 2022.

Minerva (540B) was trained on 2.7×10²⁴ FLOP, about 1.6x the compute of EXAONE 1.0 at 1.7×10²⁴ FLOP. Training compute is the closest available proxy for how much was invested in a model, though it says nothing on its own about how well that compute was spent.

These two models share no benchmark on which both have been scored, so no direct performance comparison is possible here. The specification table below is a comparison of inputs, not of results.

Specifications
LG
Organization
Google
Dec 14, 2021
Published
Jun 29, 2022
1.7×10²⁴ FLOP
Training compute1.6x
2.7×10²⁴ FLOP
300B
Parameters1.8x
540.4B
--
Dataset size
26B
--
Training hardware
Google TPU v4
--
Chips used
1,024
--
Training time
696 h
$3M
Training cost (2023 USD)
--
--
Training power draw
698.4 kW
Unreleased
Accessibility
Unreleased
No
Open weights
No
South Korea
Country
United States
Related comparisons
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.